{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of the ml_regressor() function \n",
    "\n",
    "Author: Charles Le Losq\n",
    "\n",
    "**The `rampy.mlregressor` class performs automatic data scaling, hyperparameter grid search and provides access to popular algorithms (Support Vector, Kernel Rdige, Neural Nets) for using machine learning in a regression task implying spectroscopic data.** This function allows one to link any variable to a set of spectra with using a machine learning technique. \n",
    "\n",
    "Let's assume for the sack of example that we observe spectra D that are the combination of $k$ endmember spectra S with concentrations C, such that:\n",
    "\n",
    "$$ D_{i,j} = C_{i,k} \\times S_{k,j} $$\n",
    "\n",
    "Here we assume a linear combination. In Python, assuming that the partial spectra are simple Gaussians, we can write the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "np.random.seed(42) # fixing the seed\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import rampy as rp\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples:300\n",
      "Shape of partial spectra matrix:(2, 600)\n",
      "Shape of concentration matrix:(300, 2)\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(0,600,1.0)\n",
    "nb_samples = 300 # number of samples in our dataset\n",
    "\n",
    "S_1 = norm.pdf(x,loc=200.,scale=130.)\n",
    "S_2 = norm.pdf(x,loc=400,scale=70)\n",
    "S_true = np.vstack((S_1,S_2))\n",
    "print(\"Number of samples:\"+str(nb_samples))\n",
    "print(\"Shape of partial spectra matrix:\"+str(S_true.shape))\n",
    "\n",
    "C_ = np.random.rand(nb_samples) #60 samples with random concentrations between 0 and 1\n",
    "C_true = np.vstack((C_,(1-C_))).T\n",
    "print(\"Shape of concentration matrix:\"+str(C_true.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make some observations with random noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Obs = np.dot(C_true,S_true) + np.random.randn(nb_samples,len(x))*1e-4\n",
    "\n",
    "# norm is a class which, when called, can normalize data into the\n",
    "# [0.0, 1.0] interval.\n",
    "norm = matplotlib.colors.Normalize(\n",
    "    vmin=np.min(C_),\n",
    "    vmax=np.max(C_))\n",
    "\n",
    "# choose a colormap\n",
    "c_m = matplotlib.cm.jet\n",
    "\n",
    "# create a ScalarMappable and initialize a data structure\n",
    "s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)\n",
    "s_m.set_array([])\n",
    "\n",
    "# plotting spectra\n",
    "# calling the ScalarMappable that was initialised with c_m and norm\n",
    "for i in range(C_.shape[0]):\n",
    "    plt.plot(x,\n",
    "             Obs[i,:].T,\n",
    "             color=s_m.to_rgba(C_[i]))\n",
    "\n",
    "# we plot the colorbar, using again our\n",
    "# ScalarMappable\n",
    "c_bar = plt.colorbar(s_m)\n",
    "c_bar.set_label(r\"C_\")\n",
    "\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning\n",
    "\n",
    "We can train a machine learning algorithm to follow changes in D as a function of C, assuming we measured both quantities.\n",
    "\n",
    "Rampy uses scikit_learn algorithms to do so in a easy way, with under-the-hood standardization and cross-validation. This is to use for \"simple\" projects, while more complicated things will required an ad hoc approach, directly using scikit-learn or any other relevant ML library.\n",
    "\n",
    "For now, we will show how we can train neural networks to link D and C, such that we can predict C from new observations of D.\n",
    "\n",
    "Let's first print the help to read the documentation..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class mlregressor in module rampy.ml_regressor:\n",
      "\n",
      "class mlregressor(builtins.object)\n",
      " |  use machine learning algorithms from scikit learn to perform regression between spectra and an observed variable.\n",
      " |  \n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  x : {array-like, sparse matrix}, shape = (n_samples, n_features)\n",
      " |      Spectra; n_features = n_frequencies.\n",
      " |  y : array, shape = (n_samples,)\n",
      " |      Returns predicted values.\n",
      " |  X_test : {array-like, sparse matrix}, shape = (n_samples, n_features)\n",
      " |      spectra organised in rows (1 row = one spectrum) that you want to use as a testing dataset. THose spectra should not be present in the x (training) dataset. The spectra should share a common X axis.\n",
      " |  y_test : array, shape = (n_samples,)\n",
      " |      the target that you want to use as a testing dataset. Those targets should not be present in the y (training) dataset.\n",
      " |  algorithm : String,\n",
      " |      \"KernelRidge\", \"SVM\", \"LinearRegression\", \"Lasso\", \"ElasticNet\", \"NeuralNet\", \"BaggingNeuralNet\", default = \"SVM\"\n",
      " |  scaling : Bool\n",
      " |      True or False. If True, data will be scaled during fitting and prediction with the requested scaler (see below),\n",
      " |  scaler : String\n",
      " |      the type of scaling performed. Choose between MinMaxScaler or StandardScaler, see http://scikit-learn.org/stable/modules/preprocessing.html for details. Default = \"MinMaxScaler\".\n",
      " |  test_size : float\n",
      " |      the fraction of the dataset to use as a testing dataset; only used if X_test and y_test are not provided.\n",
      " |  rand_state : Float64\n",
      " |      the random seed that is used for reproductibility of the results. Default = 42.\n",
      " |  param_kr : Dictionary\n",
      " |      contain the values of the hyperparameters that should be provided to KernelRidge and GridSearch for the Kernel Ridge regression algorithm.\n",
      " |  param_svm : Dictionary\n",
      " |      containg the values of the hyperparameters that should be provided to SVM and GridSearch for the Support Vector regression algorithm.\n",
      " |  param_neurons : Dictionary\n",
      " |      contains the parameters for the Neural Network (MLPregressor model in sklearn).\n",
      " |      Default= dict(hidden_layer_sizes=(3,),solver = 'lbfgs',activation='relu',early_stopping=True)\n",
      " |  param_bagging : Dictionary\n",
      " |      contains the parameters for the BaggingRegressor sklearn function that uses a MLPregressor base method.\n",
      " |      Default= dict(n_estimators=100, max_samples=1.0, max_features=1.0, bootstrap=True,\n",
      " |                    bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=1, random_state=rand_state, verbose=0)\n",
      " |  prediction_train : Array{Float64}\n",
      " |      the predicted target values for the training y dataset.\n",
      " |  prediction_test : Array{Float64}\n",
      " |      the predicted target values for the testing y_test dataset.\n",
      " |  model : Scikit learn model\n",
      " |      A Scikit Learn object model, see scikit learn library documentation.\n",
      " |  X_scaler :\n",
      " |      A Scikit Learn scaler object for the x values.\n",
      " |  Y_scaler :\n",
      " |      A Scikit Learn scaler object for the y values.\n",
      " |  \n",
      " |  Example\n",
      " |  -------\n",
      " |  \n",
      " |  Given an array X of n samples by m frequencies, and Y an array of n x 1 concentrations\n",
      " |  \n",
      " |  >>> model = rampy.mlregressor(X,y)\n",
      " |  >>>\n",
      " |  \n",
      " |  Remarks\n",
      " |  -------\n",
      " |  \n",
      " |  For details on hyperparameters of each algorithms, please directly consult the documentation of SciKit Learn at:\n",
      " |  \n",
      " |  http://scikit-learn.org/stable/\n",
      " |  \n",
      " |  For Support Vector and Kernel Ridge regressions, mlregressor performs a cross_validation search with using 5 KFold cross validators.\n",
      " |  \n",
      " |  If the results are poor with Support Vector and Kernel Ridge regressions, you will have to tune the param_grid_kr or param_grid_svm dictionnary that records the hyperparameter space to investigate during the cross validation.\n",
      " |  \n",
      " |  Results for machine learning algorithms can vary from run to run. A way to solve that is to fix the random_state.\n",
      " |  For neural nets, results from multiple neural nets (bagging technique) may also generalise better, such that\n",
      " |  it may be better to use the BaggingNeuralNet function.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, x, y, **kwargs)\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      x : array{Float64}\n",
      " |          the spectra organised in rows (1 row = one spectrum). The spectra should share a common X axis.\n",
      " |      y : Array{Float64}\n",
      " |          Target. Only a single target is possible for now.\n",
      " |  \n",
      " |  fit(self)\n",
      " |      Scale data and train the model with the indicated algorithm.\n",
      " |      \n",
      " |      Do not forget to tune the hyperparameters.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      algorithm : String,\n",
      " |          \"KernelRidge\", \"SVM\", \"LinearRegression\", \"Lasso\", \"ElasticNet\", \"NeuralNet\", \"BaggingNeuralNet\", default = \"SVM\"\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict using the model.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = (n_samples, n_features)\n",
      " |          Samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      C : array, shape = (n_samples,)\n",
      " |          Returns predicted values.\n",
      " |      \n",
      " |      Remark\n",
      " |      ------\n",
      " |      if self.scaling == \"yes\", scaling will be performed on the input X.\n",
      " |  \n",
      " |  refit(self)\n",
      " |      Re-train a model previously trained with fit()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### TO ADD PRIOR TO RELEASE\n",
    "help(rp.mlregressor)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choice of the algorithm\n",
    "\n",
    "Many popular algorithms are possible: [KernelRidge](http://scikit-learn.org/stable/modules/kernel_ridge.html),  [SVM](http://scikit-learn.org/stable/modules/kernel_ridge.html), [LinearRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html), [Lasso](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html), [ElasticNet](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html), [NeuralNet](http://scikit-learn.org/stable/modules/neural_networks_supervised.html), [BaggingNeuralNet](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor).\n",
    "\n",
    "LinearRegression, Lasso and ElasticNet are borned to linear problems, while KernelRidge, SVM, NeuralNet or BaggingNeuralNet can do both linear and non-linear problems. See the documentation of scikit-learn for further details, and all the articles online on those techniques. Those techniques need hyperparameters that are provided as a dictionary to `rampy.mlregressor`. I strongly encourage you to read the documentation on scikit-learn for the technique you want to use, in order to set those hyperparameters in the good range.\n",
    "\n",
    "In the present case, we are dealing with a linear problem. We thus can use the [Lasso](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) or [ElasticNet](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) algorithms.\n",
    "\n",
    "Machine learning algorithms need a training dataset, and their performance is evaluated on a \"testing\" dataset that is unseen of the algorithm. `rp.mlregressor` can split your dataset automatically, or you can choose to provide the two different datasets. Parameters for each algorithm are provided as a dictionary, see the documentation of [scikit-learn](http://scikit-learn.org/stable/index.html) for further details on them. The datasets are also standardized/normalised inside the function (if you don't know what this means, click [here](http://scikit-learn.org/stable/modules/preprocessing.html)). The type of scaling is changed by the `scaler` option.\n",
    "\n",
    "`rp.mlregressor` creates an object that possess many attributes that are set by default but can be tweaked as preferred. The object also possess two methods: fit and predict, similar to the scikit-learn API. However, contrary to scikit-learn and due to the aim of the rampy.mlregressor class, the dataset is loaded upon initialisation of the object. Fit is performed at a second stage as one can switch easily between different algorithms.\n",
    "\n",
    "The predict function works as that of a scikit-learn model, predicting new values from a new X dataset. Scaling is performed if rampy.mlregressor.scaling is set to True.\n",
    "\n",
    "For now, we are going to load our data and create our mlregressor object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = rp.mlregressor(Obs,C_true[:,0].reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This default implementation takes care of splitting the dataset in training and testing subsets in a ratio 70/30, then scaled it with a standard scaler. The test size can be seen (and then set) as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test_sz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scaler is accessed as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MinMaxScaler'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is scaling active?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to fit the data switching between the available algorithms in a loop. We use the default dictionnaries for hyperparameters, which will be optimised by gridsearch for most of the algorithms. This should be further tuned in a real-life application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in [\"KernelRidge\", \"SVM\", \"LinearRegression\", \"NeuralNet\", \"BaggingNeuralNet\"]:\n",
    "    model.algorithm = i\n",
    "    model.user_kernel = 'poly'\n",
    "    model.fit()\n",
    "    plt.figure()\n",
    "    plt.title(model.algorithm)\n",
    "    plt.plot(model.y_test,model.prediction_test,\"r.\",label=\"Test\")\n",
    "    plt.plot(model.y_train,model.prediction_train,\"k.\",label=\"Train\")\n",
    "    plt.xlabel(\"Y observed\")\n",
    "    plt.ylabel(\"Y predicted\")\n",
    "    plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The support vector machine algorithm did not work well. We can try using a linear kernel, as the default one is set to radial basis function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'poly'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.user_kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the hyperparameters are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': [1.0,\n",
       "  2.0,\n",
       "  5.0,\n",
       "  10.0,\n",
       "  50.0,\n",
       "  100.0,\n",
       "  500.0,\n",
       "  1000.0,\n",
       "  5000.0,\n",
       "  10000.0,\n",
       "  50000.0,\n",
       "  100000.0],\n",
       " 'gamma': array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
       "        1.e+04])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.param_svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can update it, and even provide other parameters if looking at sklearn help for SVM regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.param_grid_svm = dict(C= np.logspace(-5,5,40), gamma= np.logspace(-5,5,40))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also update the kernel..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.user_kernel = 'linear'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And run the training again... This will take longer as we explore a larger hyperparameter space..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6031d5ba8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.algorithm = \"SVM\"\n",
    "model.fit()\n",
    "\n",
    "plt.figure()\n",
    "plt.title(model.algorithm)\n",
    "plt.plot(model.y_test,model.prediction_test,\"r.\",label=\"Test\")\n",
    "plt.plot(model.y_train,model.prediction_train,\"k.\",label=\"Train\")\n",
    "plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "plt.xlabel(\"Y observed\")\n",
    "plt.ylabel(\"Y predicted\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We can test to refit the data with a different kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc603024be0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.user_kernel = 'poly'\n",
    "model.refit() # refit avoid running again model declaration and data standardisation.\n",
    "\n",
    "plt.figure()\n",
    "plt.title(model.algorithm)\n",
    "plt.plot(model.y_test,model.prediction_test,\"r.\",label=\"Test\")\n",
    "plt.plot(model.y_train,model.prediction_train,\"k.\",label=\"Train\")\n",
    "plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "plt.xlabel(\"Y observed\")\n",
    "plt.ylabel(\"Y predicted\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not really better. Maybe SVM is not the best technique for this problem, or we have a problem in the hyperparameters...\n",
    "\n",
    "Let's try other algorithms, like neural nets and their ensemble version.\n",
    "\n",
    "## Neural Nets\n",
    "\n",
    "We see above that our ensemble method that trains 100 neural nets and get an estimate from their mean. It seems to work well. We can play with the hyperparameters.\n",
    "\n",
    "First, we now train 1000 networks, not 100, by setting n_estimators to 1000.\n",
    "\n",
    "We also tune the architecture of the network, by putting 10 activation functions in a single hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the hidden layers:(3,)\n",
      "Activation functions in the hidden layers:relu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc602f45ba8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.algorithm = \"BaggingNeuralNet\"\n",
    "model.param_bagging = dict(n_estimators=1000, max_samples=100, max_features=len(x), bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=2, verbose=0)\n",
    "\n",
    "print(\"Shape of the hidden layers:\"+str(model.param_neurons['hidden_layer_sizes']))\n",
    "print(\"Activation functions in the hidden layers:\"+str(model.param_neurons['activation']))\n",
    "\n",
    "model.fit()\n",
    "plt.figure()\n",
    "plt.title(model.algorithm)\n",
    "plt.plot(model.y_test,model.prediction_test,\"r.\",label=\"Test\")\n",
    "plt.plot(model.y_train,model.prediction_train,\"k.\",label=\"Train\")\n",
    "plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "plt.xlabel(\"Y observed\")\n",
    "plt.ylabel(\"Y predicted\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The networks perform not very well with this setup... The [BaggingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html) uses a base model, which actually is the MLPregressor used when setting `rampy.mlregressor.algorithm = \"NeuralNets\"`. It is possible to tweak the hyperparameters of the base regressor to try improving the fit. \n",
    "\n",
    "We see above that we have 1 hidden layer with 3 RELU units. Let's have 3 layers with 10 units in each layer. This is a deeper network that can work well on complex problems. We increase the number of hidden layers (in a tuple), as described in scikit-learn [MLPregressor help](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.param_neurons['hidden_layer_sizes'] = (10,) \n",
    "model.param_neurons['activation'] = \"relu\" # we also could try changing the activation to tanh. Try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the hidden layers:(10,)\n",
      "Activation functions in the hidden layers:relu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc602e75da0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.algorithm = \"BaggingNeuralNet\"\n",
    "print(\"Shape of the hidden layers:\"+str(model.param_neurons['hidden_layer_sizes']))\n",
    "print(\"Activation functions in the hidden layers:\"+str(model.param_neurons['activation']))\n",
    "model.fit()\n",
    "plt.figure()\n",
    "plt.title(model.algorithm)\n",
    "plt.plot(model.y_test,model.prediction_test,\"r.\",label=\"Test\")\n",
    "plt.plot(model.y_train,model.prediction_train,\"k.\",label=\"Train\")\n",
    "plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "plt.xlabel(\"Y observed\")\n",
    "plt.ylabel(\"Y predicted\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting new values from this model\n",
    "\n",
    "Now we have made new observations, and we want to predict C given D. We can do that easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating 10 new observations\n",
      "Shape of concentration matrix:(10, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"generating 10 new observations\")\n",
    "C_new_ = np.random.rand(10) #10 samples with random concentrations between 0 and 1\n",
    "C_new_true = np.vstack((C_new_,(1-C_new_))).T\n",
    "print(\"Shape of concentration matrix:\"+str(C_new_true.shape))\n",
    "\n",
    "noise_new = np.random.randn(len(x))*1e-4\n",
    "Obs_new = np.dot(C_new_true,S_true) + noise_new\n",
    "\n",
    "plt.plot(x,Obs_new.T)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title(\"Our new observations in the same chemical system\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_new_predicted = model.predict(Obs_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y predicted')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title(\"New predictions with algorithm \"+model.algorithm)\n",
    "plt.plot(C_new_,C_new_predicted,\"ko\")\n",
    "plt.plot([0,1],[0,1],\"k--\",label=\"1:1 line\")\n",
    "plt.xlabel(\"Y observed\")\n",
    "plt.ylabel(\"Y predicted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean RMSE between newly predicted and observed data:0.007213124565690301\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse\n",
    "\n",
    "print(\"mean RMSE between newly predicted and observed data:\" + str(np.sqrt(mse(C_new_,C_new_predicted))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
